ABSTRACT
The objective of this chapter is to review methodological alternatives for modeling driver decision making, and to give examples of how driver decisions particularly relevant to Intelligent Transportation Systems (ITS) can be approached. The methodological techniques chosen for presentation all have the capability of being integrated into a more general predictive model of urban traffic flow. In reviewing methodological alternatives for modeling driver decision making, this chapter seeks to provide enough technical background to familiarize the reader with the basic concepts of the approach along with some of the more advanced concepts. Count data, which is data that assumes only non-negative integer values, is encountered frequently in the modeling of driver decision making. The chapter overviews a wide variety of modeling techniques that can be used to handle the types of data likely to be encountered in modeling driver decision making. The techniques in this chapter are very powerful tools for analyzing driver decision making.
